Overview

Dataset statistics

Number of variables33
Number of observations120692
Missing cells907331
Missing cells (%)22.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.3 MiB
Average record size in memory272.0 B

Variable types

Categorical14
Numeric8
DateTime3
Text5
Boolean2
Unsupported1

Alerts

appointment_status is highly overall correlated with business_name and 5 other fieldsHigh correlation
business_name is highly overall correlated with appointment_status and 3 other fieldsHigh correlation
cancelled is highly overall correlated with appointment_status and 7 other fieldsHigh correlation
case_type is highly overall correlated with opened_case_nameHigh correlation
concession_type is highly overall correlated with business_name and 3 other fieldsHigh correlation
customer_type is highly overall correlated with concession_type and 1 other fieldsHigh correlation
notice is highly overall correlated with cancelled and 1 other fieldsHigh correlation
opened_case_name is highly overall correlated with case_type and 1 other fieldsHigh correlation
patient_type is highly overall correlated with appointment_status and 4 other fieldsHigh correlation
percentage_good_appointments_and_missed_appointments_before_can is highly overall correlated with appointment_status and 4 other fieldsHigh correlation
referred is highly overall correlated with business_name and 2 other fieldsHigh correlation
session_left is highly overall correlated with opened_case_nameHigh correlation
total_closed_invoices_before_appointemnt is highly overall correlated with total_good_appointments_before_cancelled_appointmentHigh correlation
total_cxl_appointments_before_cancelled_appointment is highly overall correlated with appointment_status and 4 other fieldsHigh correlation
total_good_appointments_before_cancelled_appointment is highly overall correlated with appointment_status and 6 other fieldsHigh correlation
patient_status is highly imbalanced (87.9%)Imbalance
concession_type is highly imbalanced (60.3%)Imbalance
title is highly imbalanced (52.7%)Imbalance
state is highly imbalanced (82.9%)Imbalance
sex is highly imbalanced (62.5%)Imbalance
opened_case_name has 35575 (29.5%) missing valuesMissing
total_open_invoices_before_appointemnt has 40434 (33.5%) missing valuesMissing
session_left has 93690 (77.6%) missing valuesMissing
case_type has 35575 (29.5%) missing valuesMissing
cancelled_at has 73418 (60.8%) missing valuesMissing
billable_item has 18250 (15.1%) missing valuesMissing
category has 24615 (20.4%) missing valuesMissing
concession_type has 97703 (81.0%) missing valuesMissing
customer_type has 77964 (64.6%) missing valuesMissing
title has 53560 (44.4%) missing valuesMissing
state has 58312 (48.3%) missing valuesMissing
sex has 53523 (44.3%) missing valuesMissing
post_code has 56784 (47.0%) missing valuesMissing
city has 56373 (46.7%) missing valuesMissing
occupation has 50304 (41.7%) missing valuesMissing
total_closed_invoices_before_appointemnt has 80258 (66.5%) missing valuesMissing
total_open_invoices_before_appointemnt is highly skewed (γ1 = 24.28150002)Skewed
post_code is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_open_invoices_before_appointemnt has 79567 (65.9%) zerosZeros
total_good_appointments_before_cancelled_appointment has 77555 (64.3%) zerosZeros
total_cxl_appointments_before_cancelled_appointment has 89930 (74.5%) zerosZeros
percentage_good_appointments_and_missed_appointments_before_can has 89930 (74.5%) zerosZeros
notice has 73418 (60.8%) zerosZeros
session_left has 13663 (11.3%) zerosZeros
total_closed_invoices_before_appointemnt has 10447 (8.7%) zerosZeros

Reproduction

Analysis started2024-05-30 06:09:29.633640
Analysis finished2024-05-30 06:10:49.190157
Duration1 minute and 19.56 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

opened_case_name
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing35575
Missing (%)29.5%
Memory size1.8 MiB
Opened
47331 
Expired
23830 
Closed
13956 

Length

Max length7
Median length6
Mean length6.2799676
Min length6

Characters and Unicode

Total characters534532
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExpired
2nd rowExpired
3rd rowOpened
4th rowExpired
5th rowOpened

Common Values

ValueCountFrequency (%)
Opened 47331
39.2%
Expired 23830
19.7%
Closed 13956
 
11.6%
(Missing) 35575
29.5%

Length

2024-05-30T11:10:49.495487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:10:49.840233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
opened 47331
55.6%
expired 23830
28.0%
closed 13956
 
16.4%

Most occurring characters

ValueCountFrequency (%)
e 132448
24.8%
d 85117
15.9%
p 71161
13.3%
O 47331
 
8.9%
n 47331
 
8.9%
E 23830
 
4.5%
x 23830
 
4.5%
i 23830
 
4.5%
r 23830
 
4.5%
C 13956
 
2.6%
Other values (3) 41868
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 534532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 132448
24.8%
d 85117
15.9%
p 71161
13.3%
O 47331
 
8.9%
n 47331
 
8.9%
E 23830
 
4.5%
x 23830
 
4.5%
i 23830
 
4.5%
r 23830
 
4.5%
C 13956
 
2.6%
Other values (3) 41868
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 534532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 132448
24.8%
d 85117
15.9%
p 71161
13.3%
O 47331
 
8.9%
n 47331
 
8.9%
E 23830
 
4.5%
x 23830
 
4.5%
i 23830
 
4.5%
r 23830
 
4.5%
C 13956
 
2.6%
Other values (3) 41868
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 534532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 132448
24.8%
d 85117
15.9%
p 71161
13.3%
O 47331
 
8.9%
n 47331
 
8.9%
E 23830
 
4.5%
x 23830
 
4.5%
i 23830
 
4.5%
r 23830
 
4.5%
C 13956
 
2.6%
Other values (3) 41868
 
7.8%

total_open_invoices_before_appointemnt
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing40434
Missing (%)33.5%
Infinite0
Infinite (%)0.0%
Mean0.014602906
Minimum0
Maximum11
Zeros79567
Zeros (%)65.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-30T11:10:50.239009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.20482016
Coefficient of variation (CV)14.025987
Kurtosis830.5179
Mean0.014602906
Median Absolute Deviation (MAD)0
Skewness24.2815
Sum1172
Variance0.041951299
MonotonicityNot monotonic
2024-05-30T11:10:50.811006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 79567
65.9%
1 464
 
0.4%
2 120
 
0.1%
3 46
 
< 0.1%
4 33
 
< 0.1%
5 7
 
< 0.1%
6 6
 
< 0.1%
7 5
 
< 0.1%
8 4
 
< 0.1%
9 3
 
< 0.1%
(Missing) 40434
33.5%
ValueCountFrequency (%)
0 79567
65.9%
1 464
 
0.4%
2 120
 
0.1%
3 46
 
< 0.1%
4 33
 
< 0.1%
5 7
 
< 0.1%
6 6
 
< 0.1%
7 5
 
< 0.1%
8 4
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
11 3
 
< 0.1%
9 3
 
< 0.1%
8 4
 
< 0.1%
7 5
 
< 0.1%
6 6
 
< 0.1%
5 7
 
< 0.1%
4 33
 
< 0.1%
3 46
 
< 0.1%
2 120
 
0.1%
1 464
0.4%

total_good_appointments_before_cancelled_appointment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct169
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.2793083
Minimum0
Maximum295
Zeros77555
Zeros (%)64.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-30T11:10:51.500767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile26
Maximum295
Range295
Interquartile range (IQR)3

Descriptive statistics

Standard deviation16.78981
Coefficient of variation (CV)3.1803048
Kurtosis74.334661
Mean5.2793083
Median Absolute Deviation (MAD)0
Skewness7.2611588
Sum637165
Variance281.89771
MonotonicityNot monotonic
2024-05-30T11:10:52.124489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 77555
64.3%
2 4898
 
4.1%
1 4656
 
3.9%
3 4097
 
3.4%
4 3435
 
2.8%
5 2901
 
2.4%
6 2272
 
1.9%
7 1725
 
1.4%
8 1587
 
1.3%
9 1399
 
1.2%
Other values (159) 16166
 
13.4%
ValueCountFrequency (%)
0 77555
64.3%
1 4656
 
3.9%
2 4898
 
4.1%
3 4097
 
3.4%
4 3435
 
2.8%
5 2901
 
2.4%
6 2272
 
1.9%
7 1725
 
1.4%
8 1587
 
1.3%
9 1399
 
1.2%
ValueCountFrequency (%)
295 4
< 0.1%
294 4
< 0.1%
288 4
< 0.1%
286 8
< 0.1%
278 4
< 0.1%
271 4
< 0.1%
270 4
< 0.1%
267 4
< 0.1%
264 8
< 0.1%
261 4
< 0.1%

total_cxl_appointments_before_cancelled_appointment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct110
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1101481
Minimum0
Maximum144
Zeros89930
Zeros (%)74.5%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-30T11:10:52.748656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile11
Maximum144
Range144
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.8406378
Coefficient of variation (CV)3.7156812
Kurtosis75.060122
Mean2.1101481
Median Absolute Deviation (MAD)0
Skewness7.4665406
Sum254678
Variance61.4756
MonotonicityNot monotonic
2024-05-30T11:10:53.390563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 89930
74.5%
1 7858
 
6.5%
2 4584
 
3.8%
3 3048
 
2.5%
4 2287
 
1.9%
5 1822
 
1.5%
6 1490
 
1.2%
7 1009
 
0.8%
10 920
 
0.8%
8 872
 
0.7%
Other values (100) 6872
 
5.7%
ValueCountFrequency (%)
0 89930
74.5%
1 7858
 
6.5%
2 4584
 
3.8%
3 3048
 
2.5%
4 2287
 
1.9%
5 1822
 
1.5%
6 1490
 
1.2%
7 1009
 
0.8%
8 872
 
0.7%
9 763
 
0.6%
ValueCountFrequency (%)
144 4
 
< 0.1%
143 4
 
< 0.1%
137 4
 
< 0.1%
136 4
 
< 0.1%
134 4
 
< 0.1%
131 4
 
< 0.1%
126 8
< 0.1%
123 12
< 0.1%
118 4
 
< 0.1%
116 4
 
< 0.1%

percentage_good_appointments_and_missed_appointments_before_can
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct843
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2074556
Minimum0
Maximum100
Zeros89930
Zeros (%)74.5%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-30T11:10:53.843948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.8387097
95-th percentile57.142857
Maximum100
Range100
Interquartile range (IQR)4.8387097

Descriptive statistics

Standard deviation20.531745
Coefficient of variation (CV)2.2299043
Kurtosis6.8826289
Mean9.2074556
Median Absolute Deviation (MAD)0
Skewness2.659494
Sum1111266.2
Variance421.55254
MonotonicityNot monotonic
2024-05-30T11:10:54.172337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 89930
74.5%
33.33333333 1749
 
1.4%
50 1726
 
1.4%
100 1604
 
1.3%
25 1579
 
1.3%
20 1228
 
1.0%
16.66666667 911
 
0.8%
14.28571429 790
 
0.7%
11.11111111 649
 
0.5%
12.5 616
 
0.5%
Other values (833) 19910
 
16.5%
ValueCountFrequency (%)
0 89930
74.5%
1.612903226 12
 
< 0.1%
1.724137931 20
 
< 0.1%
1.754385965 2
 
< 0.1%
1.960784314 2
 
< 0.1%
2.083333333 2
 
< 0.1%
2.127659574 4
 
< 0.1%
2.173913043 1
 
< 0.1%
2.380952381 7
 
< 0.1%
2.43902439 9
 
< 0.1%
ValueCountFrequency (%)
100 1604
1.3%
95.45454545 21
 
< 0.1%
95.23809524 20
 
< 0.1%
93.33333333 28
 
< 0.1%
92.30769231 48
 
< 0.1%
91.89189189 34
 
< 0.1%
91.66666667 33
 
< 0.1%
91.52542373 54
 
< 0.1%
91.30434783 21
 
< 0.1%
90.90909091 51
 
< 0.1%

patient_status
Categorical

IMBALANCE 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Not Yet Actioned
114338 
Therapist Follow Up
 
3223
Has An Upcoming Booking
 
1504
Patient Stopped Treatment
 
892
Admin Follow Up
 
400
Other values (5)
 
335

Length

Max length25
Median length16
Mean length16.237033
Min length14

Characters and Unicode

Total characters1959680
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Yet Actioned
2nd rowNot Yet Actioned
3rd rowNot Yet Actioned
4th rowNot Yet Actioned
5th rowAdmin Follow Up

Common Values

ValueCountFrequency (%)
Not Yet Actioned 114338
94.7%
Therapist Follow Up 3223
 
2.7%
Has An Upcoming Booking 1504
 
1.2%
Patient Stopped Treatment 892
 
0.7%
Admin Follow Up 400
 
0.3%
On Waiting List 114
 
0.1%
Discharged By Therapist 111
 
0.1%
Do Not Contact 44
 
< 0.1%
Contacted Too Many Times 34
 
< 0.1%
Staff & Family 32
 
< 0.1%

Length

2024-05-30T11:10:54.441879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:10:54.637434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
not 114382
31.5%
actioned 114338
31.4%
yet 114338
31.4%
follow 3623
 
1.0%
up 3623
 
1.0%
therapist 3334
 
0.9%
has 1504
 
0.4%
an 1504
 
0.4%
upcoming 1504
 
0.4%
booking 1504
 
0.4%
Other values (18) 3960
 
1.1%

Most occurring characters

ValueCountFrequency (%)
t 351268
17.9%
242922
12.4%
o 241560
12.3%
e 235757
12.0%
i 122491
 
6.3%
n 121374
 
6.2%
A 116242
 
5.9%
c 116031
 
5.9%
d 115775
 
5.9%
N 114382
 
5.8%
Other values (27) 181878
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1959680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 351268
17.9%
242922
12.4%
o 241560
12.3%
e 235757
12.0%
i 122491
 
6.3%
n 121374
 
6.2%
A 116242
 
5.9%
c 116031
 
5.9%
d 115775
 
5.9%
N 114382
 
5.8%
Other values (27) 181878
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1959680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 351268
17.9%
242922
12.4%
o 241560
12.3%
e 235757
12.0%
i 122491
 
6.3%
n 121374
 
6.2%
A 116242
 
5.9%
c 116031
 
5.9%
d 115775
 
5.9%
N 114382
 
5.8%
Other values (27) 181878
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1959680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 351268
17.9%
242922
12.4%
o 241560
12.3%
e 235757
12.0%
i 122491
 
6.3%
n 121374
 
6.2%
A 116242
 
5.9%
c 116031
 
5.9%
d 115775
 
5.9%
N 114382
 
5.8%
Other values (27) 181878
9.3%

patient_type
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
New To Clinic
77567 
Recurring
38369 
new to therapist
 
2304
new injury/issue
 
1967
new to service
 
485

Length

Max length16
Median length13
Mean length11.838548
Min length9

Characters and Unicode

Total characters1428818
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRecurring
2nd rowNew To Clinic
3rd rowRecurring
4th rowRecurring
5th rowRecurring

Common Values

ValueCountFrequency (%)
New To Clinic 77567
64.3%
Recurring 38369
31.8%
new to therapist 2304
 
1.9%
new injury/issue 1967
 
1.6%
new to service 485
 
0.4%

Length

2024-05-30T11:10:54.832116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:10:55.009561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
new 82323
29.1%
to 80356
28.4%
clinic 77567
27.4%
recurring 38369
13.5%
therapist 2304
 
0.8%
injury/issue 1967
 
0.7%
service 485
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i 200226
14.0%
162679
11.4%
e 125933
8.8%
n 122659
8.6%
c 116421
 
8.1%
w 82323
 
5.8%
r 81494
 
5.7%
o 80356
 
5.6%
N 77567
 
5.4%
T 77567
 
5.4%
Other values (14) 301593
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1428818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 200226
14.0%
162679
11.4%
e 125933
8.8%
n 122659
8.6%
c 116421
 
8.1%
w 82323
 
5.8%
r 81494
 
5.7%
o 80356
 
5.6%
N 77567
 
5.4%
T 77567
 
5.4%
Other values (14) 301593
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1428818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 200226
14.0%
162679
11.4%
e 125933
8.8%
n 122659
8.6%
c 116421
 
8.1%
w 82323
 
5.8%
r 81494
 
5.7%
o 80356
 
5.6%
N 77567
 
5.4%
T 77567
 
5.4%
Other values (14) 301593
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1428818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 200226
14.0%
162679
11.4%
e 125933
8.8%
n 122659
8.6%
c 116421
 
8.1%
w 82323
 
5.8%
r 81494
 
5.7%
o 80356
 
5.6%
N 77567
 
5.4%
T 77567
 
5.4%
Other values (14) 301593
21.1%

time_of_day
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Afternoon
57831 
Morning
55857 
Evening
7004 

Length

Max length9
Median length7
Mean length7.9583237
Min length7

Characters and Unicode

Total characters960506
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMorning
2nd rowMorning
3rd rowAfternoon
4th rowAfternoon
5th rowMorning

Common Values

ValueCountFrequency (%)
Afternoon 57831
47.9%
Morning 55857
46.3%
Evening 7004
 
5.8%

Length

2024-05-30T11:10:55.221889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:10:55.383166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
afternoon 57831
47.9%
morning 55857
46.3%
evening 7004
 
5.8%

Most occurring characters

ValueCountFrequency (%)
n 241384
25.1%
o 171519
17.9%
r 113688
11.8%
e 64835
 
6.8%
i 62861
 
6.5%
g 62861
 
6.5%
A 57831
 
6.0%
f 57831
 
6.0%
t 57831
 
6.0%
M 55857
 
5.8%
Other values (2) 14008
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 960506
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 241384
25.1%
o 171519
17.9%
r 113688
11.8%
e 64835
 
6.8%
i 62861
 
6.5%
g 62861
 
6.5%
A 57831
 
6.0%
f 57831
 
6.0%
t 57831
 
6.0%
M 55857
 
5.8%
Other values (2) 14008
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 960506
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 241384
25.1%
o 171519
17.9%
r 113688
11.8%
e 64835
 
6.8%
i 62861
 
6.5%
g 62861
 
6.5%
A 57831
 
6.0%
f 57831
 
6.0%
t 57831
 
6.0%
M 55857
 
5.8%
Other values (2) 14008
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 960506
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 241384
25.1%
o 171519
17.9%
r 113688
11.8%
e 64835
 
6.8%
i 62861
 
6.5%
g 62861
 
6.5%
A 57831
 
6.0%
f 57831
 
6.0%
t 57831
 
6.0%
M 55857
 
5.8%
Other values (2) 14008
 
1.5%

day_of_week
Categorical

Distinct7
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size1.8 MiB
Thursday
25279 
Tuesday
24118 
Wednesday
23033 
Monday
20414 
Friday
20266 
Other values (2)
7580 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1086210
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTuesday
2nd rowThursday
3rd rowFriday
4th rowFriday
5th rowMonday

Common Values

ValueCountFrequency (%)
Thursday 25279
20.9%
Tuesday 24118
20.0%
Wednesday 23033
19.1%
Monday 20414
16.9%
Friday 20266
16.8%
Saturday 7514
 
6.2%
Sunday 66
 
0.1%
(Missing) 2
 
< 0.1%

Length

2024-05-30T11:10:55.539877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:10:55.663083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
thursday 25279
20.9%
tuesday 24118
20.0%
wednesday 23033
19.1%
monday 20414
16.9%
friday 20266
16.8%
saturday 7514
 
6.2%
sunday 66
 
0.1%

Most occurring characters

ValueCountFrequency (%)
203267
18.7%
d 143723
13.2%
a 128204
11.8%
y 120690
11.1%
s 72430
 
6.7%
e 70184
 
6.5%
u 56977
 
5.2%
r 53059
 
4.9%
T 49397
 
4.5%
n 43513
 
4.0%
Other values (8) 144766
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1086210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
203267
18.7%
d 143723
13.2%
a 128204
11.8%
y 120690
11.1%
s 72430
 
6.7%
e 70184
 
6.5%
u 56977
 
5.2%
r 53059
 
4.9%
T 49397
 
4.5%
n 43513
 
4.0%
Other values (8) 144766
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1086210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
203267
18.7%
d 143723
13.2%
a 128204
11.8%
y 120690
11.1%
s 72430
 
6.7%
e 70184
 
6.5%
u 56977
 
5.2%
r 53059
 
4.9%
T 49397
 
4.5%
n 43513
 
4.0%
Other values (8) 144766
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1086210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
203267
18.7%
d 143723
13.2%
a 128204
11.8%
y 120690
11.1%
s 72430
 
6.7%
e 70184
 
6.5%
u 56977
 
5.2%
r 53059
 
4.9%
T 49397
 
4.5%
n 43513
 
4.0%
Other values (8) 144766
13.3%

month_of_year
Categorical

Distinct12
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size1.8 MiB
May
12686 
March
11880 
November
11608 
April
11227 
February
11119 
Other values (7)
62170 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1086210
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNovember
2nd rowOctober
3rd rowOctober
4th rowOctober
5th rowMarch

Common Values

ValueCountFrequency (%)
May 12686
10.5%
March 11880
9.8%
November 11608
9.6%
April 11227
9.3%
February 11119
9.2%
January 10627
8.8%
December 9992
8.3%
June 9277
7.7%
August 8500
7.0%
October 8455
7.0%
Other values (2) 15319
12.7%

Length

2024-05-30T11:10:55.820271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may 12686
10.5%
march 11880
9.8%
november 11608
9.6%
april 11227
9.3%
february 11119
9.2%
january 10627
8.8%
december 9992
8.3%
june 9277
7.7%
august 8500
7.0%
october 8455
7.0%
Other values (2) 15319
12.7%

Most occurring characters

ValueCountFrequency (%)
350992
32.3%
e 104192
 
9.6%
r 93410
 
8.6%
a 56939
 
5.2%
u 55959
 
5.2%
b 48557
 
4.5%
y 42368
 
3.9%
c 30327
 
2.8%
m 28983
 
2.7%
J 27840
 
2.6%
Other values (17) 246643
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1086210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
350992
32.3%
e 104192
 
9.6%
r 93410
 
8.6%
a 56939
 
5.2%
u 55959
 
5.2%
b 48557
 
4.5%
y 42368
 
3.9%
c 30327
 
2.8%
m 28983
 
2.7%
J 27840
 
2.6%
Other values (17) 246643
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1086210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
350992
32.3%
e 104192
 
9.6%
r 93410
 
8.6%
a 56939
 
5.2%
u 55959
 
5.2%
b 48557
 
4.5%
y 42368
 
3.9%
c 30327
 
2.8%
m 28983
 
2.7%
J 27840
 
2.6%
Other values (17) 246643
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1086210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
350992
32.3%
e 104192
 
9.6%
r 93410
 
8.6%
a 56939
 
5.2%
u 55959
 
5.2%
b 48557
 
4.5%
y 42368
 
3.9%
c 30327
 
2.8%
m 28983
 
2.7%
J 27840
 
2.6%
Other values (17) 246643
22.7%

week_of_year
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean25.408427
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-30T11:10:55.969943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q112
median23
Q339
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.283636
Coefficient of variation (CV)0.60151839
Kurtosis-1.250821
Mean25.408427
Median Absolute Deviation (MAD)13
Skewness0.15891625
Sum3066543
Variance233.58953
MonotonicityNot monotonic
2024-05-30T11:10:56.153162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 2929
 
2.4%
21 2925
 
2.4%
45 2902
 
2.4%
51 2880
 
2.4%
9 2826
 
2.3%
12 2823
 
2.3%
50 2815
 
2.3%
19 2806
 
2.3%
46 2802
 
2.3%
2 2797
 
2.3%
Other values (43) 92185
76.4%
ValueCountFrequency (%)
1 1778
1.5%
2 2797
2.3%
3 2596
2.2%
4 2292
1.9%
5 2749
2.3%
6 2711
2.2%
7 2517
2.1%
8 2763
2.3%
9 2826
2.3%
10 2743
2.3%
ValueCountFrequency (%)
53 82
 
0.1%
52 868
 
0.7%
51 2880
2.4%
50 2815
2.3%
49 2649
2.2%
48 2715
2.2%
47 2652
2.2%
46 2802
2.3%
45 2902
2.4%
44 2079
1.7%

appointment_status
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Not Rebooked
89106 
Rebooked
31586 

Length

Max length12
Median length12
Mean length10.95317
Min length8

Characters and Unicode

Total characters1321960
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Rebooked
2nd rowNot Rebooked
3rd rowRebooked
4th rowRebooked
5th rowNot Rebooked

Common Values

ValueCountFrequency (%)
Not Rebooked 89106
73.8%
Rebooked 31586
 
26.2%

Length

2024-05-30T11:10:56.335487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:10:56.442906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
rebooked 120692
57.5%
not 89106
42.5%

Most occurring characters

ValueCountFrequency (%)
o 330490
25.0%
e 241384
18.3%
R 120692
 
9.1%
b 120692
 
9.1%
k 120692
 
9.1%
d 120692
 
9.1%
N 89106
 
6.7%
t 89106
 
6.7%
89106
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1321960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 330490
25.0%
e 241384
18.3%
R 120692
 
9.1%
b 120692
 
9.1%
k 120692
 
9.1%
d 120692
 
9.1%
N 89106
 
6.7%
t 89106
 
6.7%
89106
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1321960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 330490
25.0%
e 241384
18.3%
R 120692
 
9.1%
b 120692
 
9.1%
k 120692
 
9.1%
d 120692
 
9.1%
N 89106
 
6.7%
t 89106
 
6.7%
89106
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1321960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 330490
25.0%
e 241384
18.3%
R 120692
 
9.1%
b 120692
 
9.1%
k 120692
 
9.1%
d 120692
 
9.1%
N 89106
 
6.7%
t 89106
 
6.7%
89106
 
6.7%

notice
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21560
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-942.49932
Minimum-65869.135
Maximum17186.203
Zeros73418
Zeros (%)60.8%
Negative8585
Negative (%)7.1%
Memory size1.8 MiB
2024-05-30T11:10:56.558701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-65869.135
5-th percentile-5869.3219
Q10
median0
Q39.1475694
95-th percentile188.73361
Maximum17186.203
Range83055.338
Interquartile range (IQR)9.1475694

Descriptive statistics

Standard deviation5118.1669
Coefficient of variation (CV)-5.4304197
Kurtosis52.202731
Mean-942.49932
Median Absolute Deviation (MAD)0
Skewness-6.3933856
Sum-1.1375213 × 108
Variance26195633
MonotonicityNot monotonic
2024-05-30T11:10:56.730914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73418
60.8%
25.56361111 16
 
< 0.1%
39.83416667 14
 
< 0.1%
1.076111111 14
 
< 0.1%
22.41583333 14
 
< 0.1%
42.14527778 14
 
< 0.1%
43.61527778 14
 
< 0.1%
22.88638889 14
 
< 0.1%
20.045 14
 
< 0.1%
1.436666667 14
 
< 0.1%
Other values (21550) 47146
39.1%
ValueCountFrequency (%)
-65869.135 1
< 0.1%
-65863.135 1
< 0.1%
-65692.135 1
< 0.1%
-65578.635 1
< 0.1%
-65482.635 1
< 0.1%
-65476.135 1
< 0.1%
-65142.635 1
< 0.1%
-65020.635 1
< 0.1%
-64684.135 1
< 0.1%
-63829.135 1
< 0.1%
ValueCountFrequency (%)
17186.20278 4
< 0.1%
16850.20278 4
< 0.1%
16514.20278 4
< 0.1%
16178.20278 4
< 0.1%
15842.20278 4
< 0.1%
15506.20278 4
< 0.1%
15170.20278 4
< 0.1%
14834.20306 4
< 0.1%
14498.20306 4
< 0.1%
14162.20306 4
< 0.1%

session_left
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)0.1%
Missing93690
Missing (%)77.6%
Infinite0
Infinite (%)0.0%
Mean1.5521443
Minimum0
Maximum28
Zeros13663
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-30T11:10:56.857162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile6
Maximum28
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3283571
Coefficient of variation (CV)1.5000906
Kurtosis10.286529
Mean1.5521443
Median Absolute Deviation (MAD)0
Skewness2.409773
Sum41911
Variance5.4212467
MonotonicityNot monotonic
2024-05-30T11:10:56.956028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 13663
 
11.3%
1 3831
 
3.2%
2 2692
 
2.2%
3 2576
 
2.1%
4 1746
 
1.4%
5 790
 
0.7%
6 419
 
0.3%
7 384
 
0.3%
8 254
 
0.2%
11 232
 
0.2%
Other values (7) 415
 
0.3%
(Missing) 93690
77.6%
ValueCountFrequency (%)
0 13663
11.3%
1 3831
 
3.2%
2 2692
 
2.2%
3 2576
 
2.1%
4 1746
 
1.4%
5 790
 
0.7%
6 419
 
0.3%
7 384
 
0.3%
8 254
 
0.2%
9 224
 
0.2%
ValueCountFrequency (%)
28 10
 
< 0.1%
23 2
 
< 0.1%
16 8
 
< 0.1%
13 16
 
< 0.1%
12 10
 
< 0.1%
11 232
0.2%
10 145
 
0.1%
9 224
0.2%
8 254
0.2%
7 384
0.3%

case_type
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing35575
Missing (%)29.5%
Memory size1.8 MiB
Unlimited
55087 
Max Sessions
29826 
Max Amount
 
204

Length

Max length12
Median length9
Mean length10.053632
Min length9

Characters and Unicode

Total characters855735
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnlimited
2nd rowUnlimited
3rd rowUnlimited
4th rowUnlimited
5th rowUnlimited

Common Values

ValueCountFrequency (%)
Unlimited 55087
45.6%
Max Sessions 29826
24.7%
Max Amount 204
 
0.2%
(Missing) 35575
29.5%

Length

2024-05-30T11:10:57.107276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:10:57.230625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
unlimited 55087
47.8%
max 30030
26.1%
sessions 29826
25.9%
amount 204
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i 140000
16.4%
s 89478
10.5%
n 85117
9.9%
e 84913
9.9%
m 55291
 
6.5%
t 55291
 
6.5%
U 55087
 
6.4%
l 55087
 
6.4%
d 55087
 
6.4%
30030
 
3.5%
Other values (7) 150354
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 855735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 140000
16.4%
s 89478
10.5%
n 85117
9.9%
e 84913
9.9%
m 55291
 
6.5%
t 55291
 
6.5%
U 55087
 
6.4%
l 55087
 
6.4%
d 55087
 
6.4%
30030
 
3.5%
Other values (7) 150354
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 855735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 140000
16.4%
s 89478
10.5%
n 85117
9.9%
e 84913
9.9%
m 55291
 
6.5%
t 55291
 
6.5%
U 55087
 
6.4%
l 55087
 
6.4%
d 55087
 
6.4%
30030
 
3.5%
Other values (7) 150354
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 855735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 140000
16.4%
s 89478
10.5%
n 85117
9.9%
e 84913
9.9%
m 55291
 
6.5%
t 55291
 
6.5%
U 55087
 
6.4%
l 55087
 
6.4%
d 55087
 
6.4%
30030
 
3.5%
Other values (7) 150354
17.6%

cancelled_at
Date

MISSING 

Distinct14962
Distinct (%)31.6%
Missing73418
Missing (%)60.8%
Memory size1.8 MiB
Minimum2020-07-16 08:36:13+00:00
Maximum2024-05-28 08:47:23+00:00
2024-05-30T11:10:57.361776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:57.513162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct444
Distinct (%)0.4%
Missing61
Missing (%)0.1%
Memory size1.8 MiB
2024-05-30T11:10:58.011271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length106
Median length87
Mean length47.577563
Min length4

Characters and Unicode

Total characters5739329
Distinct characters86
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)< 0.1%

Sample

1st row2. Physiotherapy - 30 Minute Follow Up Consult
2nd row1. ⭐️ Physiotherapy - 45 Minute Initial Consult
3rd row2. Physiotherapy - 30 Minute Follow Up Consult
4th row2. Physiotherapy - 30 Minute Follow Up Consult
5th row2. Physiotherapy - 30 Minute Follow Up Consult
ValueCountFrequency (%)
112374
 
13.0%
consult 51921
 
6.0%
subsequent 46188
 
5.3%
2 45668
 
5.3%
consultation 45595
 
5.3%
up 44162
 
5.1%
follow 44126
 
5.1%
minute 32630
 
3.8%
physiotherapy 29759
 
3.4%
osteopathy 23229
 
2.7%
Other values (331) 390279
45.1%
2024-05-30T11:10:58.713140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
747808
 
13.0%
t 459919
 
8.0%
o 409890
 
7.1%
n 357066
 
6.2%
e 351873
 
6.1%
s 314104
 
5.5%
i 296746
 
5.2%
l 249052
 
4.3%
u 242829
 
4.2%
a 201012
 
3.5%
Other values (76) 2109030
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5739329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
747808
 
13.0%
t 459919
 
8.0%
o 409890
 
7.1%
n 357066
 
6.2%
e 351873
 
6.1%
s 314104
 
5.5%
i 296746
 
5.2%
l 249052
 
4.3%
u 242829
 
4.2%
a 201012
 
3.5%
Other values (76) 2109030
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5739329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
747808
 
13.0%
t 459919
 
8.0%
o 409890
 
7.1%
n 357066
 
6.2%
e 351873
 
6.1%
s 314104
 
5.5%
i 296746
 
5.2%
l 249052
 
4.3%
u 242829
 
4.2%
a 201012
 
3.5%
Other values (76) 2109030
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5739329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
747808
 
13.0%
t 459919
 
8.0%
o 409890
 
7.1%
n 357066
 
6.2%
e 351873
 
6.1%
s 314104
 
5.5%
i 296746
 
5.2%
l 249052
 
4.3%
u 242829
 
4.2%
a 201012
 
3.5%
Other values (76) 2109030
36.7%

billable_item
Text

MISSING 

Distinct348
Distinct (%)0.3%
Missing18250
Missing (%)15.1%
Memory size1.8 MiB
2024-05-30T11:10:59.165543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length115
Median length89
Mean length46.591915
Min length11

Characters and Unicode

Total characters4772969
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)< 0.1%

Sample

1st rowPhysiotherapy - 30 Minute Follow Up Consult
2nd rowPhysiotherapy - 45 Minute Initial Consult
3rd rowPhysiotherapy - 30 Minute Follow Up Consult
4th rowPhysiotherapy - 30 Minute Follow Up Consult
5th rowPhysiotherapy - 30 Minute Follow Up Consult
ValueCountFrequency (%)
96129
14.4%
consult 51612
 
7.7%
subsequent 46927
 
7.0%
consultation 45101
 
6.8%
up 42973
 
6.4%
follow 42910
 
6.4%
osteopathy 36693
 
5.5%
minute 32137
 
4.8%
physiotherapy 29100
 
4.4%
30 22594
 
3.4%
Other values (249) 220086
33.0%
2024-05-30T11:10:59.850738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
579642
 
12.1%
t 392528
 
8.2%
o 373034
 
7.8%
n 306944
 
6.4%
e 295966
 
6.2%
s 282801
 
5.9%
i 260542
 
5.5%
l 246385
 
5.2%
u 243169
 
5.1%
a 197942
 
4.1%
Other values (60) 1594016
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4772969
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
579642
 
12.1%
t 392528
 
8.2%
o 373034
 
7.8%
n 306944
 
6.4%
e 295966
 
6.2%
s 282801
 
5.9%
i 260542
 
5.5%
l 246385
 
5.2%
u 243169
 
5.1%
a 197942
 
4.1%
Other values (60) 1594016
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4772969
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
579642
 
12.1%
t 392528
 
8.2%
o 373034
 
7.8%
n 306944
 
6.4%
e 295966
 
6.2%
s 282801
 
5.9%
i 260542
 
5.5%
l 246385
 
5.2%
u 243169
 
5.1%
a 197942
 
4.1%
Other values (60) 1594016
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4772969
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
579642
 
12.1%
t 392528
 
8.2%
o 373034
 
7.8%
n 306944
 
6.4%
e 295966
 
6.2%
s 282801
 
5.9%
i 260542
 
5.5%
l 246385
 
5.2%
u 243169
 
5.1%
a 197942
 
4.1%
Other values (60) 1594016
33.4%

category
Text

MISSING 

Distinct139
Distinct (%)0.1%
Missing24615
Missing (%)20.4%
Memory size1.8 MiB
2024-05-30T11:11:00.491787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length68
Median length60
Mean length33.025146
Min length3

Characters and Unicode

Total characters3172957
Distinct characters90
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowA | PHYSIOTHERAPY
2nd rowA | PHYSIOTHERAPY
3rd rowA | PHYSIOTHERAPY
4th rowA | PHYSIOTHERAPY
5th rowA | PHYSIOTHERAPY
ValueCountFrequency (%)
55387
 
11.6%
2 36191
 
7.5%
subsequent 30047
 
6.3%
consultations 29138
 
6.1%
for 28877
 
6.0%
osteopathy 28582
 
6.0%
existing 28464
 
5.9%
patients 28464
 
5.9%
physiotherapy 18041
 
3.8%
a 17257
 
3.6%
Other values (191) 178957
37.3%
2024-05-30T11:11:01.071653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
385066
 
12.1%
t 251135
 
7.9%
s 219566
 
6.9%
e 176794
 
5.6%
n 160850
 
5.1%
o 145076
 
4.6%
i 139802
 
4.4%
a 135318
 
4.3%
P 103035
 
3.2%
u 98842
 
3.1%
Other values (80) 1357473
42.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3172957
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
385066
 
12.1%
t 251135
 
7.9%
s 219566
 
6.9%
e 176794
 
5.6%
n 160850
 
5.1%
o 145076
 
4.6%
i 139802
 
4.4%
a 135318
 
4.3%
P 103035
 
3.2%
u 98842
 
3.1%
Other values (80) 1357473
42.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3172957
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
385066
 
12.1%
t 251135
 
7.9%
s 219566
 
6.9%
e 176794
 
5.6%
n 160850
 
5.1%
o 145076
 
4.6%
i 139802
 
4.4%
a 135318
 
4.3%
P 103035
 
3.2%
u 98842
 
3.1%
Other values (80) 1357473
42.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3172957
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
385066
 
12.1%
t 251135
 
7.9%
s 219566
 
6.9%
e 176794
 
5.6%
n 160850
 
5.1%
o 145076
 
4.6%
i 139802
 
4.4%
a 135318
 
4.3%
P 103035
 
3.2%
u 98842
 
3.1%
Other values (80) 1357473
42.8%

cancelled
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
False
73418 
True
47274 
ValueCountFrequency (%)
False 73418
60.8%
True 47274
39.2%
2024-05-30T11:11:01.206132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Distinct47410
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2009-07-28 22:45:00+00:00
Maximum2025-12-23 00:45:00+00:00
2024-05-30T11:11:01.317331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:11:01.460884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

business_name
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Scarb Physio & Health
24911 
Osteopathy | Melton
23208 
⬅️ Front Desk Migration
16650 
Osteopathy | Bacchus Marsh
15055 
All Sorted Physiotherapy
9661 
Other values (18)
31207 

Length

Max length30
Median length26
Mean length21.396141
Min length12

Characters and Unicode

Total characters2582343
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScarb Physio & Health
2nd rowScarb Physio & Health
3rd rowScarb Physio & Health
4th rowScarb Physio & Health
5th rowScarb Physio & Health

Common Values

ValueCountFrequency (%)
Scarb Physio & Health 24911
20.6%
Osteopathy | Melton 23208
19.2%
⬅️ Front Desk Migration 16650
13.8%
Osteopathy | Bacchus Marsh 15055
12.5%
All Sorted Physiotherapy 9661
 
8.0%
Newport Psychology 7289
 
6.0%
Scarb Remedial Massage 6753
 
5.6%
Newport Physiotherapy 6437
 
5.3%
Bosch Psychology 4511
 
3.7%
PhysioCall Gladstone 1842
 
1.5%
Other values (13) 4375
 
3.6%

Length

2024-05-30T11:11:01.597568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
66509
16.7%
osteopathy 38263
 
9.6%
scarb 32403
 
8.1%
health 25400
 
6.4%
melton 25339
 
6.3%
physio 24911
 
6.2%
physiotherapy 17326
 
4.3%
⬅️ 16650
 
4.2%
front 16650
 
4.2%
desk 16650
 
4.2%
Other values (25) 119029
29.8%

Most occurring characters

ValueCountFrequency (%)
278438
 
10.8%
t 203754
 
7.9%
o 197072
 
7.6%
a 189317
 
7.3%
h 173678
 
6.7%
e 171250
 
6.6%
s 167484
 
6.5%
y 124746
 
4.8%
r 122194
 
4.7%
l 96087
 
3.7%
Other values (37) 858323
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2582343
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
278438
 
10.8%
t 203754
 
7.9%
o 197072
 
7.6%
a 189317
 
7.3%
h 173678
 
6.7%
e 171250
 
6.6%
s 167484
 
6.5%
y 124746
 
4.8%
r 122194
 
4.7%
l 96087
 
3.7%
Other values (37) 858323
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2582343
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
278438
 
10.8%
t 203754
 
7.9%
o 197072
 
7.6%
a 189317
 
7.3%
h 173678
 
6.7%
e 171250
 
6.6%
s 167484
 
6.5%
y 124746
 
4.8%
r 122194
 
4.7%
l 96087
 
3.7%
Other values (37) 858323
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2582343
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
278438
 
10.8%
t 203754
 
7.9%
o 197072
 
7.6%
a 189317
 
7.3%
h 173678
 
6.7%
e 171250
 
6.6%
s 167484
 
6.5%
y 124746
 
4.8%
r 122194
 
4.7%
l 96087
 
3.7%
Other values (37) 858323
33.2%

concession_type
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct17
Distinct (%)0.1%
Missing97703
Missing (%)81.0%
Memory size1.8 MiB
Concession Discount
13652 
Concession / Pension Card Holder
5724 
VIP Discount
1867 
Student
 
1294
VIP + Concession Discount
 
315
Other values (12)
 
137

Length

Max length32
Median length19
Mean length21.06129
Min length3

Characters and Unicode

Total characters484178
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConcession / Pension Card Holder
2nd rowConcession / Pension Card Holder
3rd rowConcession / Pension Card Holder
4th rowConcession / Pension Card Holder
5th rowConcession / Pension Card Holder

Common Values

ValueCountFrequency (%)
Concession Discount 13652
 
11.3%
Concession / Pension Card Holder 5724
 
4.7%
VIP Discount 1867
 
1.5%
Student 1294
 
1.1%
VIP + Concession Discount 315
 
0.3%
WorkCover 27
 
< 0.1%
Patient is under 18 years old 22
 
< 0.1%
Students | Under 18 Years Old 19
 
< 0.1%
Medicare 16
 
< 0.1%
Third Party 13
 
< 0.1%
Other values (7) 40
 
< 0.1%
(Missing) 97703
81.0%

Length

2024-05-30T11:11:01.741594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
concession 19708
31.5%
discount 15834
25.3%
6065
 
9.7%
card 5734
 
9.2%
pension 5724
 
9.1%
holder 5724
 
9.1%
vip 2182
 
3.5%
student 1294
 
2.1%
under 41
 
0.1%
old 41
 
0.1%
Other values (15) 254
 
0.4%

Most occurring characters

ValueCountFrequency (%)
n 68120
14.1%
o 66809
13.8%
s 61102
12.6%
i 41361
8.5%
39612
8.2%
c 35561
7.3%
e 32685
6.8%
C 25472
 
5.3%
t 18521
 
3.8%
u 17171
 
3.5%
Other values (27) 77764
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 484178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 68120
14.1%
o 66809
13.8%
s 61102
12.6%
i 41361
8.5%
39612
8.2%
c 35561
7.3%
e 32685
6.8%
C 25472
 
5.3%
t 18521
 
3.8%
u 17171
 
3.5%
Other values (27) 77764
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 484178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 68120
14.1%
o 66809
13.8%
s 61102
12.6%
i 41361
8.5%
39612
8.2%
c 35561
7.3%
e 32685
6.8%
C 25472
 
5.3%
t 18521
 
3.8%
u 17171
 
3.5%
Other values (27) 77764
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 484178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 68120
14.1%
o 66809
13.8%
s 61102
12.6%
i 41361
8.5%
39612
8.2%
c 35561
7.3%
e 32685
6.8%
C 25472
 
5.3%
t 18521
 
3.8%
u 17171
 
3.5%
Other values (27) 77764
16.1%

customer_type
Categorical

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)0.1%
Missing77964
Missing (%)64.6%
Memory size1.8 MiB
WorkCover
6918 
Medicare Referrals
5887 
NDIS
4528 
CDM
4438 
Concession / Pension Card Holder
4330 
Other values (27)
16627 

Length

Max length32
Median length29
Mean length11.55968
Min length3

Characters and Unicode

Total characters493922
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConcession / Pension Card Holder
2nd rowConcession / Pension Card Holder
3rd rowConcession / Pension Card Holder
4th rowConcession / Pension Card Holder
5th rowConcession / Pension Card Holder

Common Values

ValueCountFrequency (%)
WorkCover 6918
 
5.7%
Medicare Referrals 5887
 
4.9%
NDIS 4528
 
3.8%
CDM 4438
 
3.7%
Concession / Pension Card Holder 4330
 
3.6%
DVA 4191
 
3.5%
DVA Card Holder 2891
 
2.4%
Medicare 1883
 
1.6%
NDIS Customer 1489
 
1.2%
Third Party 1445
 
1.2%
Other values (22) 4728
 
3.9%
(Missing) 77964
64.6%

Length

2024-05-30T11:11:01.874242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
medicare 7770
9.8%
card 7225
9.1%
holder 7221
9.1%
dva 7082
8.9%
workcover 6952
 
8.8%
ndis 6017
 
7.6%
referrals 5887
 
7.4%
5122
 
6.4%
cdm 4438
 
5.6%
concession 4341
 
5.5%
Other values (34) 17386
21.9%

Most occurring characters

ValueCountFrequency (%)
e 56373
 
11.4%
r 55221
 
11.2%
37307
 
7.6%
o 36908
 
7.5%
d 26129
 
5.3%
C 25948
 
5.3%
a 25190
 
5.1%
s 21736
 
4.4%
i 20049
 
4.1%
n 19815
 
4.0%
Other values (36) 169246
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 493922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 56373
 
11.4%
r 55221
 
11.2%
37307
 
7.6%
o 36908
 
7.5%
d 26129
 
5.3%
C 25948
 
5.3%
a 25190
 
5.1%
s 21736
 
4.4%
i 20049
 
4.1%
n 19815
 
4.0%
Other values (36) 169246
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 493922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 56373
 
11.4%
r 55221
 
11.2%
37307
 
7.6%
o 36908
 
7.5%
d 26129
 
5.3%
C 25948
 
5.3%
a 25190
 
5.1%
s 21736
 
4.4%
i 20049
 
4.1%
n 19815
 
4.0%
Other values (36) 169246
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 493922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 56373
 
11.4%
r 55221
 
11.2%
37307
 
7.6%
o 36908
 
7.5%
d 26129
 
5.3%
C 25948
 
5.3%
a 25190
 
5.1%
s 21736
 
4.4%
i 20049
 
4.1%
n 19815
 
4.0%
Other values (36) 169246
34.3%

title
Categorical

IMBALANCE  MISSING 

Distinct28
Distinct (%)< 0.1%
Missing53560
Missing (%)44.4%
Memory size1.8 MiB
Mr
22269 
Mrs
18159 
Ms
14851 
Miss
7712 
 
1818
Other values (23)
2323 

Length

Max length15
Median length2
Mean length2.5545046
Min length1

Characters and Unicode

Total characters171489
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowMrs
2nd rowMrs
3rd rowMr
4th rowMr
5th rowMr

Common Values

ValueCountFrequency (%)
Mr 22269
18.5%
Mrs 18159
 
15.0%
Ms 14851
 
12.3%
Miss 7712
 
6.4%
1818
 
1.5%
Master 956
 
0.8%
Dr 616
 
0.5%
MISS 291
 
0.2%
Mrs 128
 
0.1%
Mr 112
 
0.1%
Other values (18) 220
 
0.2%
(Missing) 53560
44.4%

Length

2024-05-30T11:11:02.001000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mr 22414
34.3%
mrs 18289
28.0%
ms 14884
22.8%
miss 8004
 
12.3%
master 1057
 
1.6%
dr 616
 
0.9%
sr 12
 
< 0.1%
no 10
 
< 0.1%
title 10
 
< 0.1%
please 10
 
< 0.1%
Other values (5) 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M 64656
37.7%
s 49575
28.9%
r 42281
24.7%
i 7724
 
4.5%
2227
 
1.3%
e 994
 
0.6%
t 983
 
0.6%
a 974
 
0.6%
S 697
 
0.4%
D 616
 
0.4%
Other values (16) 762
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 64656
37.7%
s 49575
28.9%
r 42281
24.7%
i 7724
 
4.5%
2227
 
1.3%
e 994
 
0.6%
t 983
 
0.6%
a 974
 
0.6%
S 697
 
0.4%
D 616
 
0.4%
Other values (16) 762
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 64656
37.7%
s 49575
28.9%
r 42281
24.7%
i 7724
 
4.5%
2227
 
1.3%
e 994
 
0.6%
t 983
 
0.6%
a 974
 
0.6%
S 697
 
0.4%
D 616
 
0.4%
Other values (16) 762
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 64656
37.7%
s 49575
28.9%
r 42281
24.7%
i 7724
 
4.5%
2227
 
1.3%
e 994
 
0.6%
t 983
 
0.6%
a 974
 
0.6%
S 697
 
0.4%
D 616
 
0.4%
Other values (16) 762
 
0.4%

state
Categorical

IMBALANCE  MISSING 

Distinct27
Distinct (%)< 0.1%
Missing58312
Missing (%)48.3%
Memory size1.8 MiB
QLD
54693 
VIC
 
3541
Qld
 
2269
Vic
 
576
NSW
 
487
Other values (22)
 
814

Length

Max length13
Median length3
Mean length3.0088971
Min length2

Characters and Unicode

Total characters187695
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowQLD
2nd rowQLD
3rd rowQLD
4th rowQLD
5th rowQLD

Common Values

ValueCountFrequency (%)
QLD 54693
45.3%
VIC 3541
 
2.9%
Qld 2269
 
1.9%
Vic 576
 
0.5%
NSW 487
 
0.4%
QLD 292
 
0.2%
VIc 158
 
0.1%
VIC 105
 
0.1%
VIC 58
 
< 0.1%
Qld 57
 
< 0.1%
Other values (17) 144
 
0.1%
(Missing) 58312
48.3%

Length

2024-05-30T11:11:02.135653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
qld 57346
91.9%
vic 4488
 
7.2%
nsw 487
 
0.8%
wa 25
 
< 0.1%
act 9
 
< 0.1%
sa 8
 
< 0.1%
melton 4
 
< 0.1%
nt 3
 
< 0.1%
viv 3
 
< 0.1%
west 3
 
< 0.1%
Other values (7) 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
Q 57313
30.5%
L 54987
29.3%
D 54985
29.3%
V 4446
 
2.4%
I 3865
 
2.1%
C 3713
 
2.0%
l 2366
 
1.3%
d 2365
 
1.3%
c 784
 
0.4%
i 628
 
0.3%
Other values (28) 2243
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 187695
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q 57313
30.5%
L 54987
29.3%
D 54985
29.3%
V 4446
 
2.4%
I 3865
 
2.1%
C 3713
 
2.0%
l 2366
 
1.3%
d 2365
 
1.3%
c 784
 
0.4%
i 628
 
0.3%
Other values (28) 2243
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 187695
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q 57313
30.5%
L 54987
29.3%
D 54985
29.3%
V 4446
 
2.4%
I 3865
 
2.1%
C 3713
 
2.0%
l 2366
 
1.3%
d 2365
 
1.3%
c 784
 
0.4%
i 628
 
0.3%
Other values (28) 2243
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 187695
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q 57313
30.5%
L 54987
29.3%
D 54985
29.3%
V 4446
 
2.4%
I 3865
 
2.1%
C 3713
 
2.0%
l 2366
 
1.3%
d 2365
 
1.3%
c 784
 
0.4%
i 628
 
0.3%
Other values (28) 2243
 
1.2%
Distinct14588
Distinct (%)12.2%
Missing925
Missing (%)0.8%
Memory size1.8 MiB
Minimum1909-01-31 00:00:00
Maximum2024-04-01 00:00:00
2024-05-30T11:11:02.257584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:11:02.405658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sex
Categorical

IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing53523
Missing (%)44.3%
Memory size1.8 MiB
Female
44050 
Male
22826 
female
 
186
male
 
81
AMAB
 
22

Length

Max length6
Median length6
Mean length5.3172148
Min length4

Characters and Unicode

Total characters357152
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 44050
36.5%
Male 22826
18.9%
female 186
 
0.2%
male 81
 
0.1%
AMAB 22
 
< 0.1%
Other 4
 
< 0.1%
(Missing) 53523
44.3%

Length

2024-05-30T11:11:02.562138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:11:02.678867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
female 44236
65.9%
male 22907
34.1%
amab 22
 
< 0.1%
other 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 111383
31.2%
a 67143
18.8%
l 67143
18.8%
m 44317
 
12.4%
F 44050
 
12.3%
M 22848
 
6.4%
f 186
 
0.1%
A 44
 
< 0.1%
B 22
 
< 0.1%
O 4
 
< 0.1%
Other values (3) 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 357152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 111383
31.2%
a 67143
18.8%
l 67143
18.8%
m 44317
 
12.4%
F 44050
 
12.3%
M 22848
 
6.4%
f 186
 
0.1%
A 44
 
< 0.1%
B 22
 
< 0.1%
O 4
 
< 0.1%
Other values (3) 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 357152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 111383
31.2%
a 67143
18.8%
l 67143
18.8%
m 44317
 
12.4%
F 44050
 
12.3%
M 22848
 
6.4%
f 186
 
0.1%
A 44
 
< 0.1%
B 22
 
< 0.1%
O 4
 
< 0.1%
Other values (3) 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 357152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 111383
31.2%
a 67143
18.8%
l 67143
18.8%
m 44317
 
12.4%
F 44050
 
12.3%
M 22848
 
6.4%
f 186
 
0.1%
A 44
 
< 0.1%
B 22
 
< 0.1%
O 4
 
< 0.1%
Other values (3) 12
 
< 0.1%

post_code
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing56784
Missing (%)47.0%
Memory size1.8 MiB

city
Text

MISSING 

Distinct633
Distinct (%)1.0%
Missing56373
Missing (%)46.7%
Memory size1.8 MiB
2024-05-30T11:11:03.213676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length27
Median length21
Mean length9.4763756
Min length2

Characters and Unicode

Total characters609511
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)0.2%

Sample

1st rowKIPPA RING
2nd rowKIPPA RING
3rd rowScarborough
4th rowScarborough
5th rowScarborough
ValueCountFrequency (%)
scarborough 14138
18.3%
newport 8383
 
10.8%
redcliffe 6613
 
8.5%
gladstone 4965
 
6.4%
margate 3067
 
4.0%
bay 2702
 
3.5%
deception 2686
 
3.5%
clontarf 2641
 
3.4%
kippa 2180
 
2.8%
ring 2180
 
2.8%
Other values (430) 27901
36.0%
2024-05-30T11:11:03.843959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 52609
 
8.6%
r 46177
 
7.6%
e 38746
 
6.4%
a 37660
 
6.2%
t 23336
 
3.8%
c 23088
 
3.8%
g 21967
 
3.6%
S 21263
 
3.5%
i 20719
 
3.4%
l 20062
 
3.3%
Other values (58) 303884
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 609511
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 52609
 
8.6%
r 46177
 
7.6%
e 38746
 
6.4%
a 37660
 
6.2%
t 23336
 
3.8%
c 23088
 
3.8%
g 21967
 
3.6%
S 21263
 
3.5%
i 20719
 
3.4%
l 20062
 
3.3%
Other values (58) 303884
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 609511
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 52609
 
8.6%
r 46177
 
7.6%
e 38746
 
6.4%
a 37660
 
6.2%
t 23336
 
3.8%
c 23088
 
3.8%
g 21967
 
3.6%
S 21263
 
3.5%
i 20719
 
3.4%
l 20062
 
3.3%
Other values (58) 303884
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 609511
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 52609
 
8.6%
r 46177
 
7.6%
e 38746
 
6.4%
a 37660
 
6.2%
t 23336
 
3.8%
c 23088
 
3.8%
g 21967
 
3.6%
S 21263
 
3.5%
i 20719
 
3.4%
l 20062
 
3.3%
Other values (58) 303884
49.9%

occupation
Text

MISSING 

Distinct3136
Distinct (%)4.5%
Missing50304
Missing (%)41.7%
Memory size1.8 MiB
2024-05-30T11:11:04.315544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length56
Median length44
Mean length12.023754
Min length1

Characters and Unicode

Total characters846328
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique437 ?
Unique (%)0.6%

Sample

1st rowRetired
2nd rowRetired
3rd rowDraftsman
4th rowDraftsman
5th rowDraftsman
ValueCountFrequency (%)
retired 13366
 
11.8%
manager 4049
 
3.6%
student 3974
 
3.5%
teacher 3405
 
3.0%
worker 3295
 
2.9%
nurse 2311
 
2.0%
support 2134
 
1.9%
officer 2106
 
1.9%
assistant 1929
 
1.7%
driver 1792
 
1.6%
Other values (1768) 74870
66.1%
2024-05-30T11:11:04.849057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 113390
13.4%
r 84824
 
10.0%
t 68858
 
8.1%
i 64207
 
7.6%
a 51824
 
6.1%
n 46908
 
5.5%
43876
 
5.2%
o 38416
 
4.5%
s 35944
 
4.2%
d 32063
 
3.8%
Other values (67) 266018
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 846328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 113390
13.4%
r 84824
 
10.0%
t 68858
 
8.1%
i 64207
 
7.6%
a 51824
 
6.1%
n 46908
 
5.5%
43876
 
5.2%
o 38416
 
4.5%
s 35944
 
4.2%
d 32063
 
3.8%
Other values (67) 266018
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 846328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 113390
13.4%
r 84824
 
10.0%
t 68858
 
8.1%
i 64207
 
7.6%
a 51824
 
6.1%
n 46908
 
5.5%
43876
 
5.2%
o 38416
 
4.5%
s 35944
 
4.2%
d 32063
 
3.8%
Other values (67) 266018
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 846328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 113390
13.4%
r 84824
 
10.0%
t 68858
 
8.1%
i 64207
 
7.6%
a 51824
 
6.1%
n 46908
 
5.5%
43876
 
5.2%
o 38416
 
4.5%
s 35944
 
4.2%
d 32063
 
3.8%
Other values (67) 266018
31.4%

referred
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
False
80292 
True
40400 
ValueCountFrequency (%)
False 80292
66.5%
True 40400
33.5%
2024-05-30T11:11:04.974605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

total_closed_invoices_before_appointemnt
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct174
Distinct (%)0.4%
Missing80258
Missing (%)66.5%
Infinite0
Infinite (%)0.0%
Mean12.050354
Minimum0
Maximum305
Zeros10447
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-30T11:11:05.092876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q312
95-th percentile58
Maximum305
Range305
Interquartile range (IQR)12

Descriptive statistics

Standard deviation25.594842
Coefficient of variation (CV)2.1239909
Kurtosis34.544534
Mean12.050354
Median Absolute Deviation (MAD)3
Skewness5.0288834
Sum487244
Variance655.09592
MonotonicityNot monotonic
2024-05-30T11:11:05.246363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10447
 
8.7%
1 3817
 
3.2%
2 3279
 
2.7%
3 2916
 
2.4%
4 2114
 
1.8%
5 1751
 
1.5%
6 1379
 
1.1%
7 1062
 
0.9%
8 1048
 
0.9%
9 978
 
0.8%
Other values (164) 11643
 
9.6%
(Missing) 80258
66.5%
ValueCountFrequency (%)
0 10447
8.7%
1 3817
 
3.2%
2 3279
 
2.7%
3 2916
 
2.4%
4 2114
 
1.8%
5 1751
 
1.5%
6 1379
 
1.1%
7 1062
 
0.9%
8 1048
 
0.9%
9 978
 
0.8%
ValueCountFrequency (%)
305 4
< 0.1%
303 4
< 0.1%
297 4
< 0.1%
295 4
< 0.1%
294 4
< 0.1%
286 4
< 0.1%
280 4
< 0.1%
278 4
< 0.1%
275 4
< 0.1%
273 4
< 0.1%

Interactions

2024-05-30T11:10:41.029592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:32.138845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:33.244100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:34.377123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:36.095862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:37.409775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:38.591799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:39.992535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:41.125667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:32.269421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:33.375418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:34.513665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:36.289512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:37.606415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:38.768502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:40.111712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:41.249015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:32.398587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:33.541164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:34.700048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:36.452889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:37.726616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:38.909615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:40.273163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:41.379319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:32.592289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:33.681715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:35.330723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:36.624521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:37.880737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:39.074753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:40.409433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:41.590004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:32.730035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:33.811380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:35.491335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:36.781555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:38.012630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:39.221184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:40.526985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:41.825324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:32.861395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:33.993346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:35.652358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:36.926813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:38.160260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:39.364048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:40.643977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:42.042348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:33.006724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:34.148665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:35.780087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:37.122251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:38.261631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:39.707227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:40.807164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:42.242208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:33.147492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:34.266774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:35.926767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:37.265652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:38.390601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:39.859664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:10:40.927604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-30T11:11:05.396728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
appointment_statusbusiness_namecancelledcase_typeconcession_typecustomer_typeday_of_weekmonth_of_yearnoticeopened_case_namepatient_statuspatient_typepercentage_good_appointments_and_missed_appointments_before_canreferredsession_leftsexstatetime_of_daytitletotal_closed_invoices_before_appointemnttotal_cxl_appointments_before_cancelled_appointmenttotal_good_appointments_before_cancelled_appointmenttotal_open_invoices_before_appointemntweek_of_year
appointment_status1.0000.5080.7420.0430.3980.3330.0590.0520.4060.1660.1600.7280.6040.1040.0080.0650.1810.0860.1530.0770.6260.7310.342-0.015
business_name0.5081.0000.6200.1950.5050.2930.1210.0820.1750.2900.0990.321-0.2160.794-0.0210.0870.2510.1760.1470.439-0.194-0.119-0.0540.010
cancelled0.7420.6201.0000.0410.5070.3870.0760.0500.6120.2510.2940.9290.7190.1660.0860.0530.2390.0920.197NaN0.7190.9000.305-0.033
case_type0.0430.1950.0411.0000.1040.2720.0710.0240.0000.5190.0260.045-0.0340.122NaN0.0150.0350.0610.0360.008-0.031-0.025-0.012-0.013
concession_type0.3980.5050.5070.1041.0000.6510.0750.054-0.3140.2680.0860.244-0.2500.8000.0010.0870.3180.1570.191-0.184-0.253-0.312-0.0010.000
customer_type0.3330.2930.3870.2720.6511.0000.1290.068-0.0470.2670.0900.2010.0090.5430.0850.2390.2680.1880.2030.0710.021-0.0050.091-0.016
day_of_week0.0590.1210.0760.0710.0750.1291.0000.0330.0270.0540.0370.0460.0080.115-0.0160.0390.0590.1460.0490.0550.0120.023-0.022-0.012
month_of_year0.0520.0820.0500.0240.0540.0680.0331.000-0.0400.0500.0550.0480.0030.0780.0020.0130.0450.0200.030-0.0020.006-0.022-0.0050.298
notice0.4060.1750.6120.000-0.314-0.0470.027-0.0401.0000.0620.0330.1900.1980.1590.0740.0170.0430.0450.1360.3720.2310.5780.288-0.044
opened_case_name0.1660.2900.2510.5190.2680.2670.0540.0500.0621.0000.0350.165-0.1400.2360.8990.0290.1440.0770.077-0.037-0.143-0.1530.004-0.015
patient_status0.1600.0990.2940.0260.0860.0900.0370.0550.0330.0351.0000.1600.0710.1410.0130.0290.0410.0320.0350.0660.0740.109-0.034-0.058
patient_type0.7280.3210.9290.0450.2440.2010.0460.0480.1900.1650.1601.0000.6910.1820.0750.0350.1060.0680.0950.1950.7080.9530.312-0.014
percentage_good_appointments_and_missed_appointments_before_can0.604-0.2160.719-0.034-0.2500.0090.0080.0030.198-0.1400.0710.6911.0000.1540.0620.0460.0710.0580.113-0.1210.9820.7290.320-0.001
referred0.1040.7940.1660.1220.8000.5430.1150.0780.1590.2360.1410.1820.1541.000-0.0180.0580.3290.1560.2710.2760.0780.1670.0510.007
session_left0.008-0.0210.086NaN0.0010.085-0.0160.0020.0740.8990.0130.0750.062-0.0181.0000.1020.0430.0440.092-0.1150.0480.0450.073-0.007
sex0.0650.0870.0530.0150.0870.2390.0390.0130.0170.0290.0290.0350.0460.0580.1021.0000.1010.0210.4950.015-0.062-0.0370.0430.010
state0.1810.2510.2390.0350.3180.2680.0590.0450.0430.1440.0410.1060.0710.3290.0430.1011.0000.0960.074-0.084-0.165-0.1730.001-0.032
time_of_day0.0860.1760.0920.0610.1570.1880.1460.0200.0450.0770.0320.0680.0580.1560.0440.0210.0961.0000.0770.0970.0300.0480.0150.002
title0.1530.1470.1970.0360.1910.2030.0490.0300.1360.0770.0350.0950.1130.2710.0920.4950.0740.0771.0000.0630.1050.114-0.012-0.028
total_closed_invoices_before_appointemnt0.0770.439NaN0.008-0.1840.0710.055-0.0020.372-0.0370.0660.195-0.1210.276-0.1150.015-0.0840.0970.0631.0000.2660.772NaN0.022
total_cxl_appointments_before_cancelled_appointment0.626-0.1940.719-0.031-0.2530.0210.0120.0060.231-0.1430.0740.7080.9820.0780.048-0.062-0.1650.0300.1050.2661.0000.7800.3240.005
total_good_appointments_before_cancelled_appointment0.731-0.1190.900-0.025-0.312-0.0050.023-0.0220.578-0.1530.1090.9530.7290.1670.045-0.037-0.1730.0480.1140.7720.7801.0000.325-0.013
total_open_invoices_before_appointemnt0.342-0.0540.305-0.012-0.0010.091-0.022-0.0050.2880.004-0.0340.3120.3200.0510.0730.0430.0010.015-0.012NaN0.3240.3251.000-0.008
week_of_year-0.0150.010-0.033-0.0130.000-0.016-0.0120.298-0.044-0.015-0.058-0.014-0.0010.007-0.0070.010-0.0320.002-0.0280.0220.005-0.013-0.0081.000

Missing values

2024-05-30T11:10:42.873315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-30T11:10:44.712256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-30T11:10:47.610237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

opened_case_nametotal_open_invoices_before_appointemnttotal_good_appointments_before_cancelled_appointmenttotal_cxl_appointments_before_cancelled_appointmentpercentage_good_appointments_and_missed_appointments_before_canpatient_statuspatient_typetime_of_dayday_of_weekmonth_of_yearweek_of_yearappointment_statusnoticesession_leftcase_typecancelled_atappointment_typebillable_itemcategorycancelledappointment_start_timebusiness_nameconcession_typecustomer_typetitlestatedate_of_birthsexpost_codecityoccupationreferredtotal_closed_invoices_before_appointemnt
0Expired0.01.000.0Not Yet ActionedRecurringMorningTuesdayNovember46.0Not Rebooked2.054167NaNUnlimited2022-11-14 22:26:45+002. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYYes2022-11-15 00:30:00+00Scarb Physio & HealthConcession / Pension Card HolderConcession / Pension Card HolderMrsQLD1928-07-20Female4020.0KIPPA RINGRetiredYesNaN
1Expired0.00.000.0Not Yet ActionedNew To ClinicMorningThursdayOctober42.0Not Rebooked0.000000NaNUnlimitedNaN1. ⭐️ Physiotherapy - 45 Minute Initial ConsultPhysiotherapy - 45 Minute Initial ConsultA | PHYSIOTHERAPYNo2022-10-20 00:30:00+00Scarb Physio & HealthConcession / Pension Card HolderConcession / Pension Card HolderMrsQLD1928-07-20Female4020.0KIPPA RINGRetiredYesNaN
2Opened0.08.000.0Not Yet ActionedRecurringAfternoonFridayOctober43.0Rebooked26.688333NaNUnlimited2022-10-27 00:48:42+002. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYYes2022-10-28 03:30:00+00Scarb Physio & HealthNaNNaNMrQLD1956-06-19Male4020.0ScarboroughDraftsmanYesNaN
3Expired0.08.000.0Not Yet ActionedRecurringAfternoonFridayOctober43.0Rebooked26.688333NaNUnlimited2022-10-27 00:48:42+002. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYYes2022-10-28 03:30:00+00Scarb Physio & HealthNaNNaNMrQLD1956-06-19Male4020.0ScarboroughDraftsmanYesNaN
5Opened0.09.0110.0Admin Follow UpRecurringMorningMondayMarch13.0Not Rebooked1.770556NaNUnlimited2024-03-24 21:43:46+002. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYYes2024-03-24 23:30:00+00Scarb Physio & HealthNaNNaNMrQLD1956-06-19Male4020.0ScarboroughDraftsmanYesNaN
6Expired0.09.0110.0Admin Follow UpRecurringMorningMondayMarch13.0Not Rebooked1.770556NaNUnlimited2024-03-24 21:43:46+002. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYYes2024-03-24 23:30:00+00Scarb Physio & HealthNaNNaNMrQLD1956-06-19Male4020.0ScarboroughDraftsmanYesNaN
8Expired0.00.000.0Not Yet ActionedNew To ClinicMorningMondayMarch13.0Not Rebooked0.000000NaNUnlimitedNaN2. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYNo2022-03-28 01:00:00+00Scarb Physio & HealthNaNNaNMrQLD1956-06-19Male4020.0ScarboroughDraftsmanYesNaN
9Opened0.00.000.0Not Yet ActionedNew To ClinicMorningSaturdayOctober41.0Not Rebooked0.000000NaNUnlimitedNaN2. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYNo2022-10-14 23:30:00+00Scarb Physio & HealthNaNNaNMrQLD1956-06-19Male4020.0ScarboroughDraftsmanYesNaN
11Expired0.00.000.0Not Yet ActionedNew To ClinicMorningTuesdayApril15.0Not Rebooked0.000000NaNUnlimitedNaN2. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYNo2022-04-11 21:30:00+00Scarb Physio & HealthNaNNaNMrQLD1956-06-19Male4020.0ScarboroughDraftsmanYesNaN
12Expired0.00.000.0Not Yet ActionedNew To ClinicMorningMondayMarch12.0Not Rebooked0.000000NaNUnlimitedNaN2. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYNo2022-03-21 00:30:00+00Scarb Physio & HealthNaNNaNMrQLD1956-06-19Male4020.0ScarboroughDraftsmanYesNaN
opened_case_nametotal_open_invoices_before_appointemnttotal_good_appointments_before_cancelled_appointmenttotal_cxl_appointments_before_cancelled_appointmentpercentage_good_appointments_and_missed_appointments_before_canpatient_statuspatient_typetime_of_dayday_of_weekmonth_of_yearweek_of_yearappointment_statusnoticesession_leftcase_typecancelled_atappointment_typebillable_itemcategorycancelledappointment_start_timebusiness_nameconcession_typecustomer_typetitlestatedate_of_birthsexpost_codecityoccupationreferredtotal_closed_invoices_before_appointemnt
160213ExpiredNaN12.000.0Not Yet ActionedRecurringAfternoonTuesdayFebruary7.0Not Rebooked28.2008333.0Max Sessions2023-02-13 01:47:57+002. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYYes2023-02-14 06:00:00+00Scarb Physio & HealthNaNNaNMrsQLD1976-06-05Female4020.0ScarboroughValidation EngineerYes12.0
160214OpenedNaN12.000.0Not Yet ActionedRecurringAfternoonTuesdayFebruary7.0Not Rebooked28.200833NaNUnlimited2023-02-13 01:47:57+002. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYYes2023-02-14 06:00:00+00Scarb Physio & HealthNaNNaNMrsQLD1976-06-05Female4020.0ScarboroughValidation EngineerYes12.0
160215ExpiredNaN12.000.0Not Yet ActionedRecurringAfternoonTuesdayFebruary7.0Not Rebooked28.200833NaNUnlimited2023-02-13 01:47:57+002. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYYes2023-02-14 06:00:00+00Scarb Physio & HealthNaNNaNMrsQLD1976-06-05Female4020.0ScarboroughValidation EngineerYes12.0
160216ClosedNaN4.000.0Not Yet ActionedRecurringAfternoonMondayJune23.0Rebooked20.5491670.0Max Sessions2023-06-04 10:17:03+002. GOOPP Pre-paid 20 Minute Treatment (redeem voucher)PhysioCall - Get Out Of Pain Plan (GOOPP)B | Get Out Of Pain Pre-Paid Sessions - (GOOPP)Yes2023-06-05 06:50:00+00PhysioCall GladstoneNaNNaNMrQLD2000-07-26NaN4680.0Glen EdenElectricianYes4.0
160217ExpiredNaN0.000.0Not Yet ActionedNew To ClinicAfternoonThursdayOctober41.0Not Rebooked28.228056NaNUnlimited2022-10-12 01:01:19+001. ⭐️ Physiotherapy - 45 Minute Initial ConsultPhysiotherapy - 45 Minute Initial ConsultA | PHYSIOTHERAPYYes2022-10-13 05:15:00+00Scarb Physio & HealthNaNNaNMrNaN1983-05-03MaleNaNNaNNaNNo0.0
160218ExpiredNaN2.000.0Not Yet ActionedRecurringMorningMondayOctober43.0Rebooked63.668056NaNUnlimited2022-10-21 05:49:55+002. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYYes2022-10-23 21:30:00+00Scarb Physio & HealthStudentStudentMrQLD2006-04-24Male4020.0KIPPA RINGStudentYes2.0
160219ExpiredNaN3.0125.0Not Yet ActionedRecurringMorningThursdayNovember44.0Not Rebooked14.337778NaNUnlimited2022-11-02 07:09:44+002. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYYes2022-11-02 21:30:00+00Scarb Physio & HealthStudentStudentMrQLD2006-04-24Male4020.0KIPPA RINGStudentYes3.0
160220ClosedNaN5.000.0Not Yet ActionedRecurringAfternoonThursdayJune24.0Rebooked28.7780560.0Max Sessions2023-06-14 01:43:19+002. GOOPP Pre-paid 20 Minute Treatment (redeem voucher)PhysioCall - Get Out Of Pain Plan (GOOPP)B | Get Out Of Pain Pre-Paid Sessions - (GOOPP)Yes2023-06-15 06:30:00+00PhysioCall GladstoneNaNNaNNaNNaN1981-12-10NaNNaNNaNNaNNo4.0
160221OpenedNaN5.000.0Not Yet ActionedRecurringAfternoonThursdayJune24.0Rebooked28.7780561.0Max Sessions2023-06-14 01:43:19+002. GOOPP Pre-paid 20 Minute Treatment (redeem voucher)PhysioCall - Get Out Of Pain Plan (GOOPP)B | Get Out Of Pain Pre-Paid Sessions - (GOOPP)Yes2023-06-15 06:30:00+00PhysioCall GladstoneNaNNaNNaNNaN1981-12-10NaNNaNNaNNaNNo4.0
160222ExpiredNaN1.000.0Not Yet ActionedRecurringAfternoonFridayOctober41.0Not Rebooked24.880556NaNUnlimited2022-10-13 02:07:10+002. Physiotherapy - 30 Minute Follow Up ConsultPhysiotherapy - 30 Minute Follow Up ConsultA | PHYSIOTHERAPYYes2022-10-14 03:00:00+00Scarb Physio & HealthNaNNaNMsQLD1987-02-07Female4504.0NarangbaOffice managerYes1.0